As early as , Breiman proposed the method of bagging [ 56 ], and five years later, he further proposed the method of Random Forest [ 57 ]. Freund proposed the AdaBoost method in [ 58 ], and with the continuous improvement of machine learning classifiers, in , Chen et al. As an application, recently Reece et al.
Regression analysis often has certain requirements on the independent variables, such as the absence of multicollinearity, however ensemble learning classification methods relax the constraints on independent variables. The numbers of sending and not sending messages are unbalanced in the dataset, and the larger set is subsampled randomly to obtain a set the same size as the smaller one.
We find that the error rates of Random Forest and AdaBoost are the lowest for females sending messages to males while XGBoost is the lowest for males sending messages to females. Attribute importance ranking is shown in Figs. Similarly, Fig.
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Attribute relative importance rankings when women send messages to men for different classification methods. The horizontal axis indicates the attributes and the vertical axis indicates their corresponding importance. For bagging, Random Forest, and AdaBoost, the relative importance of each variable in the classification task is measured by the Gini index, and for XGBoost the relative importance is measured by the Gain parameter. Attribute relative importance rankings when men send messages to women for different classification methods.
The purpose of ensemble learning classification is different from logistic regression analysis. According to Figs. The concept of strategic behavior [ 61 ] derives from economics, where the original implication is that firms take action that affects the market environment to increase profits referring to the message response rate in this study , which is then extended to matching problems [ 35 ], such as mate matching.
Since without user response data, we would like to use centrality indices characterizing user popularity to analyze whether users tend to send messages to people who are more popular than themselves or to those who are less popular. For males sending messages to females, there exist weak positive correlations between centrality indices, which can be characterized by small positive and significant correlation coefficients, while for females sending messages to males, there exist weak or modest positive correlations between centrality indices characterized by small or slightly larger positive and significant correlation coefficients.
Men do not show strategic behavior to a large extent when sending messages, while for women, as their centrality indices increase, the corresponding indices of men who received their messages could also increase. By studying the correlations between the same centrality index pairs for users, we further analyze whether users tend to send messages to people who are more popular than themselves or to those who are less popular. As a comparison, we also give the randomized results. Compared with men, more women tend to send messages to people who are more popular than themselves.
Some studies have found a significant positive correlation between the popularity of male and female users. For example, the research by Taylor et al. For example, the research on users in Boston and San Diego did not find evidence of strategic behavior [ 33 , 34 ]. Another research on online dating data from a midsized southwestern city in the U. We find that users on different platforms or in different cultural contexts have different strategic behaviors, and the underlying mechanisms still need to be explored further.
10 Online Dating Statistics You Should Know
In summary, we analyze online dating data to reveal the differences of choice preference between men and women and the important factors affecting potential mate choice. When considering centrality indices, we find that for women, the popularity and activity of the men they contact are significantly positively associated with their messaging behaviors, while for men only the popularity of the women they contact are significantly positively associated with their messaging behaviors.
At the same time, we also find that compared with men, women attach greater importance to the socio-economic status of potential partners and their own socio-economic status will affect their enthusiasm for interaction with potential mates. The machine learning classification methods are used to find the important factors predicting messaging behaviors. At last strategic behavior is analyzed and we find that there are different strategic behaviors for men and women.
Although users do not know the centrality indices of themselves and their potential partners, compared with men, for women sending messages there is a stronger positive correlation between the centrality indices of women and men, and more women are inclined to send messages to people more popular than themselves. This paper provides a foundation for gender-specific preference of potential mate choice in online dating.
On the one hand, this study can provide references for the online dating sites to design better recommendation systems. On the other hand, an in-depth understanding of mate preference, such as the compatibility scores, can help users to select the most appropriate and reliable mates. There are still some limitations for the paper.
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In fact, BMI can compensate for the disadvantages of wages or education [ 65 ]. Secondly, we only have the message sending data and lack the reply data, which makes it impossible for us to study the interaction between users. Ranking effects caused by recommendation algorithms in online environments have been shown to influence the music people select [ 66 ] and the politicians people favor [ 67 ].
In real life, sending a message to another user is usually not affected by a single attribute. Fifthly, there are significant differences between Chinese and western cultures, and the website is only for heterosexual users, thus the conclusions of this paper may not be applicable to western society or homosexual people [ 68 , 69 ].
There are several avenues for future research. We can examine the influence of recommendation algorithms on potential mate choice in online dating. We can also use the results obtained in the paper to further study the problem of stable matching for potential mate choice. The compatibility score between a female preference and the profile of the corresponding other side. The compatibility score between a male preference and the profile of the corresponding other side. Hu H, Wang X Evolution of a large online social network.
Europhys Lett 86, Soc Netw Anal Min Psychol Sci Public Interest — Rosenfeld MJ Marriage, choice, and couplehood in the age of the Internet. Sociol Sci — Proc Natl Acad Sci — Schwarz S, Hassebrauck M Sex and age differences in mate-selection preferences.
Hum Nat — Examining gender differences of online identity reconstruction in terms of vanity. Buss DM Sex differences in human mate preferences: evolutionary hypotheses tested in 37 cultures. Behav Brain Sci — Trivers R Parental investment and sexual selection.
Biological Laboratories, Harvard University, Cambridge. Male and female strategies in romantic partner choice. Am Sociol Rev — Stauder J Friendship networks and the social structure of opportunities for contact and interaction. Soc Sci Res — Lin KH, Lundquist J Mate selection in cyberspace: the intersection of race, gender, and education.
Am J Sociol — Lewis K Preferences in the early stages of mate choice. Soc Forces — Educational homophily in online mate selection. Eur Sociol Rev — Annu Rev Sociol — EPJ Data Sci Brooks JE, Neville HA Interracial attraction among college men: the influence of ideologies, familiarity, and similarity. Manag Sci — Pollak RA How bargaining in marriage drives marriage market equilibrium. Am Econ Rev — Quant Mark Econ — Games Econ Behav — Lee S, Niederle M Propose with a rose? Signaling in Internet dating markets.
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Exp Econ — Ong D, Wang J Income attraction: an online dating field experiment. Lecture notes in social networks. In: Proceedings of the fourth ACM conference on recommender systems. User Model User-Adapt Interact — In: Proceedings of the 23rd international conference on world wide web. Szell M, Thurner S How women organize social networks different from men. Sci Rep In: The 49th Hawaii international conference on system sciences. Choo E, Siow A Who marries whom and why.
Dunn MJ, Brinton S, Clark L Universal sex differences in online advertisers age preferences: comparing data from 14 cultures and 2 religious groups. Evol Hum Behav — An examination of height preferences in romantic coupling.
Gender-specific preference in online dating
Ward J What are you doing on Tinder? Impression management on a matchmaking mobile app. Inf Commun Soc — Ellison N, Heino R, Gibbs J Managing impressions online: self-presentation processes in the online dating environment. Pers Soc Psychol Bull — Breiman L Bagging predictors. Mach Learn — Breiman L Random forests.